Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations913599
Missing cells43008
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory174.3 MiB
Average record size in memory200.0 B

Variable types

Numeric14
DateTime3
Categorical3

Alerts

Breadth is highly overall correlated with Draught and 1 other fieldsHigh correlation
COG is highly overall correlated with THHigh correlation
Destination is highly overall correlated with EndLatitude and 4 other fieldsHigh correlation
Draught is highly overall correlated with Breadth and 1 other fieldsHigh correlation
EndLatitude is highly overall correlated with Destination and 7 other fieldsHigh correlation
EndLongitude is highly overall correlated with Destination and 7 other fieldsHigh correlation
EndPort is highly overall correlated with Destination and 7 other fieldsHigh correlation
Latitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
Length is highly overall correlated with Breadth and 1 other fieldsHigh correlation
Longitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
StartLatitude is highly overall correlated with EndLatitude and 6 other fieldsHigh correlation
StartLongitude is highly overall correlated with Destination and 7 other fieldsHigh correlation
StartPort is highly overall correlated with Destination and 7 other fieldsHigh correlation
TH is highly overall correlated with COGHigh correlation
Destination is highly imbalanced (55.3%) Imbalance
Length has 10070 (1.1%) missing values Missing
Breadth has 10127 (1.1%) missing values Missing
Draught has 16303 (1.8%) missing values Missing

Reproduction

Analysis started2025-06-06 00:26:37.814760
Analysis finished2025-06-06 00:27:20.027631
Duration42.21 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

TripID
Real number (ℝ)

Distinct1126
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1125612.4
Minimum5944
Maximum2278147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:20.079575image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum5944
5-th percentile55365
Q1473272
median1170717
Q31669988
95-th percentile2160044
Maximum2278147
Range2272203
Interquartile range (IQR)1196716

Descriptive statistics

Standard deviation673813.44
Coefficient of variation (CV)0.59861943
Kurtosis-1.2739594
Mean1125612.4
Median Absolute Deviation (MAD)546619
Skewness-0.039518375
Sum1.0283583 × 1012
Variance4.5402456 × 1011
MonotonicityNot monotonic
2025-06-06T02:27:20.163336image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2183472 2609
 
0.3%
1669988 2362
 
0.3%
265994 2085
 
0.2%
1778056 2068
 
0.2%
2201111 2059
 
0.2%
1993462 2053
 
0.2%
1019076 2036
 
0.2%
2183480 2026
 
0.2%
624032 1994
 
0.2%
1677413 1989
 
0.2%
Other values (1116) 892318
97.7%
ValueCountFrequency (%)
5944 1237
0.1%
10257 577
 
0.1%
19002 41
 
< 0.1%
19585 1646
0.2%
23834 1241
0.1%
24805 595
 
0.1%
25124 659
0.1%
28257 1280
0.1%
29139 1247
0.1%
29152 1296
0.1%
ValueCountFrequency (%)
2278147 518
0.1%
2278140 588
0.1%
2278125 528
0.1%
2278114 574
0.1%
2278113 574
0.1%
2278085 522
0.1%
2278084 522
0.1%
2278083 522
0.1%
2278070 494
0.1%
2278069 494
0.1%

StartLatitude
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.033009
Minimum53.33
Maximum54.54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:20.225389image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum53.33
5-th percentile53.52
Q153.58
median54.36
Q354.36
95-th percentile54.36
Maximum54.54
Range1.21
Interquartile range (IQR)0.78

Descriptive statistics

Standard deviation0.39140826
Coefficient of variation (CV)0.0072438731
Kurtosis-1.8378418
Mean54.033009
Median Absolute Deviation (MAD)0
Skewness-0.36154275
Sum49364503
Variance0.15320042
MonotonicityNot monotonic
2025-06-06T02:27:20.291646image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
54.36 525482
57.5%
53.58 168718
 
18.5%
53.59 56719
 
6.2%
53.51 34008
 
3.7%
53.57 26593
 
2.9%
53.53 23437
 
2.6%
53.6 21983
 
2.4%
53.52 10380
 
1.1%
53.55 5686
 
0.6%
53.56 5650
 
0.6%
Other values (18) 34943
 
3.8%
ValueCountFrequency (%)
53.33 3169
 
0.3%
53.34 2535
 
0.3%
53.5 1285
 
0.1%
53.51 34008
3.7%
53.52 10380
 
1.1%
53.53 23437
2.6%
53.54 1288
 
0.1%
53.55 5686
 
0.6%
53.56 5650
 
0.6%
53.57 26593
2.9%
ValueCountFrequency (%)
54.54 3667
 
0.4%
54.49 552
 
0.1%
54.37 1257
 
0.1%
54.36 525482
57.5%
54.33 3161
 
0.3%
54.31 1158
 
0.1%
53.75 976
 
0.1%
53.74 530
 
0.1%
53.67 2150
 
0.2%
53.66 411
 
< 0.1%

StartLongitude
Real number (ℝ)

High correlation 

Distinct34
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4652222
Minimum8.14
Maximum10.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:20.364064image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum8.14
5-th percentile8.5
Q18.52
median10.14
Q310.14
95-th percentile10.14
Maximum10.34
Range2.2
Interquartile range (IQR)1.62

Descriptive statistics

Standard deviation0.80563974
Coefficient of variation (CV)0.085115776
Kurtosis-1.8490833
Mean9.4652222
Median Absolute Deviation (MAD)0
Skewness-0.36018461
Sum8647417.6
Variance0.64905539
MonotonicityNot monotonic
2025-06-06T02:27:20.447900image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10.14 498287
54.5%
8.52 160444
 
17.6%
8.53 78601
 
8.6%
8.51 36432
 
4.0%
8.57 31343
 
3.4%
10.13 24011
 
2.6%
8.54 15226
 
1.7%
8.5 11982
 
1.3%
8.15 10964
 
1.2%
8.49 9062
 
1.0%
Other values (24) 37247
 
4.1%
ValueCountFrequency (%)
8.14 485
 
0.1%
8.15 10964
1.2%
8.16 1297
 
0.1%
8.19 976
 
0.1%
8.22 530
 
0.1%
8.36 1078
 
0.1%
8.37 1072
 
0.1%
8.38 411
 
< 0.1%
8.39 984
 
0.1%
8.4 523
 
0.1%
ValueCountFrequency (%)
10.34 552
 
0.1%
10.29 3667
 
0.4%
10.18 3188
 
0.3%
10.17 2933
 
0.3%
10.16 1485
 
0.2%
10.15 1154
 
0.1%
10.14 498287
54.5%
10.13 24011
 
2.6%
8.57 31343
 
3.4%
8.56 3146
 
0.3%
Distinct953
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 06:03:00+00:00
Maximum2017-05-26 19:44:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-06T02:27:20.528240image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:20.604666image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

EndLatitude
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.084079
Minimum53.47
Maximum54.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:20.670528image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum53.47
5-th percentile53.5
Q153.53
median54.38
Q354.53
95-th percentile54.54
Maximum54.64
Range1.17
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.47536514
Coefficient of variation (CV)0.008789373
Kurtosis-1.8529125
Mean54.084079
Median Absolute Deviation (MAD)0.16
Skewness-0.3132826
Sum49411160
Variance0.22597202
MonotonicityNot monotonic
2025-06-06T02:27:20.824519image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
54.54 217507
23.8%
53.53 194398
21.3%
53.5 87533
9.6%
54.38 85200
 
9.3%
54.52 54381
 
6.0%
54.53 40517
 
4.4%
53.54 37790
 
4.1%
54.44 33365
 
3.7%
53.52 31820
 
3.5%
54.43 24028
 
2.6%
Other values (19) 107060
11.7%
ValueCountFrequency (%)
53.47 1290
 
0.1%
53.48 677
 
0.1%
53.49 9439
 
1.0%
53.5 87533
9.6%
53.51 10829
 
1.2%
53.52 31820
 
3.5%
53.53 194398
21.3%
53.54 37790
 
4.1%
53.56 678
 
0.1%
53.61 2018
 
0.2%
ValueCountFrequency (%)
54.64 1646
 
0.2%
54.59 740
 
0.1%
54.54 217507
23.8%
54.53 40517
 
4.4%
54.52 54381
 
6.0%
54.51 5163
 
0.6%
54.5 522
 
0.1%
54.47 1280
 
0.1%
54.46 7688
 
0.8%
54.45 8797
 
1.0%

EndLongitude
Real number (ℝ)

High correlation 

Distinct47
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.99502
Minimum9.5
Maximum18.92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:20.895607image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile9.9
Q19.93
median18.5
Q318.6
95-th percentile18.71
Maximum18.92
Range9.42
Interquartile range (IQR)8.67

Descriptive statistics

Standard deviation4.2753283
Coefficient of variation (CV)0.28511654
Kurtosis-1.8773544
Mean14.99502
Median Absolute Deviation (MAD)0.17
Skewness-0.34813669
Sum13699435
Variance18.278432
MonotonicityNot monotonic
2025-06-06T02:27:20.971452image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
18.51 115647
12.7%
18.5 101860
 
11.1%
9.91 100740
 
11.0%
9.93 76722
 
8.4%
9.9 60305
 
6.6%
18.65 45248
 
5.0%
9.95 44441
 
4.9%
18.66 44309
 
4.8%
18.67 37755
 
4.1%
9.82 27616
 
3.0%
Other values (37) 258956
28.3%
ValueCountFrequency (%)
9.5 1189
 
0.1%
9.51 661
 
0.1%
9.54 1196
 
0.1%
9.55 178
 
< 0.1%
9.56 644
 
0.1%
9.73 678
 
0.1%
9.82 27616
3.0%
9.83 6963
 
0.8%
9.88 1221
 
0.1%
9.9 60305
6.6%
ValueCountFrequency (%)
18.92 1646
 
0.2%
18.88 740
 
0.1%
18.83 7385
 
0.8%
18.82 11996
1.3%
18.81 4884
 
0.5%
18.75 1501
 
0.2%
18.72 3774
 
0.4%
18.71 27107
3.0%
18.7 7897
 
0.9%
18.68 3368
 
0.4%
Distinct943
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 14:36:00+00:00
Maximum2017-05-27 22:11:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-06T02:27:21.047732image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:21.124422image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

StartPort
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.5 KiB
KIEL
535277 
BREMERHAVEN
378322 

Length

Max length11
Median length4
Mean length6.898705
Min length4

Characters and Unicode

Total characters6302650
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBREMERHAVEN
2nd rowBREMERHAVEN
3rd rowBREMERHAVEN
4th rowBREMERHAVEN
5th rowBREMERHAVEN

Common Values

ValueCountFrequency (%)
KIEL 535277
58.6%
BREMERHAVEN 378322
41.4%

Length

2025-06-06T02:27:21.195029image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T02:27:21.241793image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
kiel 535277
58.6%
bremerhaven 378322
41.4%

Most occurring characters

ValueCountFrequency (%)
E 1670243
26.5%
R 756644
12.0%
K 535277
 
8.5%
I 535277
 
8.5%
L 535277
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6302650
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1670243
26.5%
R 756644
12.0%
K 535277
 
8.5%
I 535277
 
8.5%
L 535277
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6302650
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1670243
26.5%
R 756644
12.0%
K 535277
 
8.5%
I 535277
 
8.5%
L 535277
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6302650
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1670243
26.5%
R 756644
12.0%
K 535277
 
8.5%
I 535277
 
8.5%
L 535277
 
8.5%
B 378322
 
6.0%
M 378322
 
6.0%
H 378322
 
6.0%
A 378322
 
6.0%
V 378322
 
6.0%

EndPort
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size892.5 KiB
GDYNIA
535277 
HAMBURG
378322 

Length

Max length7
Median length6
Mean length6.4141007
Min length6

Characters and Unicode

Total characters5859916
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHAMBURG
2nd rowHAMBURG
3rd rowHAMBURG
4th rowHAMBURG
5th rowHAMBURG

Common Values

ValueCountFrequency (%)
GDYNIA 535277
58.6%
HAMBURG 378322
41.4%

Length

2025-06-06T02:27:21.289337image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-06-06T02:27:21.326696image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
ValueCountFrequency (%)
gdynia 535277
58.6%
hamburg 378322
41.4%

Most occurring characters

ValueCountFrequency (%)
G 913599
15.6%
A 913599
15.6%
D 535277
9.1%
Y 535277
9.1%
N 535277
9.1%
I 535277
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5859916
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
G 913599
15.6%
A 913599
15.6%
D 535277
9.1%
Y 535277
9.1%
N 535277
9.1%
I 535277
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5859916
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
G 913599
15.6%
A 913599
15.6%
D 535277
9.1%
Y 535277
9.1%
N 535277
9.1%
I 535277
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5859916
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
G 913599
15.6%
A 913599
15.6%
D 535277
9.1%
Y 535277
9.1%
N 535277
9.1%
I 535277
9.1%
H 378322
6.5%
M 378322
6.5%
B 378322
6.5%
U 378322
6.5%

time
Date

Distinct414193
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
Minimum2016-01-13 06:03:00+00:00
Maximum2017-05-27 22:11:00+00:00
Invalid dates0
Invalid dates (%)0.0%
2025-06-06T02:27:21.381699image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:21.458265image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

shiptype
Real number (ℝ)

Distinct10
Distinct (%)< 0.1%
Missing6440
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean72.271913
Minimum69
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:21.516541image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum69
5-th percentile70
Q170
median71
Q371
95-th percentile80
Maximum89
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.5290157
Coefficient of variation (CV)0.048829698
Kurtosis0.88312527
Mean72.271913
Median Absolute Deviation (MAD)1
Skewness1.5964718
Sum65562116
Variance12.453952
MonotonicityNot monotonic
2025-06-06T02:27:21.569394image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
71 406083
44.4%
70 311250
34.1%
79 122970
 
13.5%
81 34528
 
3.8%
72 12776
 
1.4%
80 10865
 
1.2%
73 3625
 
0.4%
74 3334
 
0.4%
69 1144
 
0.1%
89 584
 
0.1%
(Missing) 6440
 
0.7%
ValueCountFrequency (%)
69 1144
 
0.1%
70 311250
34.1%
71 406083
44.4%
72 12776
 
1.4%
73 3625
 
0.4%
74 3334
 
0.4%
79 122970
 
13.5%
80 10865
 
1.2%
81 34528
 
3.8%
89 584
 
0.1%
ValueCountFrequency (%)
89 584
 
0.1%
81 34528
 
3.8%
80 10865
 
1.2%
79 122970
 
13.5%
74 3334
 
0.4%
73 3625
 
0.4%
72 12776
 
1.4%
71 406083
44.4%
70 311250
34.1%
69 1144
 
0.1%

Length
Real number (ℝ)

High correlation  Missing 

Distinct98
Distinct (%)< 0.1%
Missing10070
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean128.53889
Minimum45
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:21.638176image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum45
5-th percentile74
Q189
median134
Q3154
95-th percentile171
Maximum399
Range354
Interquartile range (IQR)65

Descriptive statistics

Standard deviation42.623176
Coefficient of variation (CV)0.33159751
Kurtosis6.7830954
Mean128.53889
Median Absolute Deviation (MAD)30
Skewness1.5032217
Sum1.1613862 × 108
Variance1816.7352
MonotonicityNot monotonic
2025-06-06T02:27:21.719765image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
151 88721
 
9.7%
134 77476
 
8.5%
88 60876
 
6.7%
155 58691
 
6.4%
125 47042
 
5.1%
168 40460
 
4.4%
82 34232
 
3.7%
79 34121
 
3.7%
154 32693
 
3.6%
68 31700
 
3.5%
Other values (88) 397517
43.5%
ValueCountFrequency (%)
45 1760
 
0.2%
65 1648
 
0.2%
66 1509
 
0.2%
68 31700
3.5%
70 5412
 
0.6%
74 9892
 
1.1%
75 10068
 
1.1%
79 34121
3.7%
80 1606
 
0.2%
81 8616
 
0.9%
ValueCountFrequency (%)
399 1716
0.2%
397 1153
 
0.1%
396 660
 
0.1%
300 1142
 
0.1%
299 537
 
0.1%
295 1252
 
0.1%
294 2171
0.2%
293 1214
 
0.1%
277 1629
0.2%
275 3297
0.4%

Breadth
Real number (ℝ)

High correlation  Missing 

Distinct34
Distinct (%)< 0.1%
Missing10127
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean20.288646
Minimum8
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:21.793077image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile12
Q113
median22
Q324
95-th percentile27
Maximum60
Range52
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.1808664
Coefficient of variation (CV)0.30464656
Kurtosis5.16364
Mean20.288646
Median Absolute Deviation (MAD)3
Skewness0.93156625
Sum18330224
Variance38.20311
MonotonicityNot monotonic
2025-06-06T02:27:21.861603image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
22 134856
14.8%
23 110567
12.1%
12 105533
11.6%
24 100922
11.0%
13 95408
10.4%
25 67083
7.3%
26 49595
 
5.4%
18 42349
 
4.6%
19 38460
 
4.2%
27 26307
 
2.9%
Other values (24) 132392
14.5%
ValueCountFrequency (%)
8 1760
 
0.2%
9 637
 
0.1%
10 3499
 
0.4%
11 23892
 
2.6%
12 105533
11.6%
13 95408
10.4%
14 13141
 
1.4%
15 14020
 
1.5%
16 12775
 
1.4%
17 1662
 
0.2%
ValueCountFrequency (%)
60 1716
 
0.2%
59 660
 
0.1%
56 1153
 
0.1%
48 1142
 
0.1%
42 1071
 
0.1%
40 3919
 
0.4%
36 652
 
0.1%
35 564
 
0.1%
33 681
 
0.1%
32 13768
1.5%

Draught
Real number (ℝ)

High correlation  Missing 

Distinct237
Distinct (%)< 0.1%
Missing16303
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean6.4696078
Minimum0.1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.019094image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile2.8
Q14.59
median7.2
Q38.3
95-th percentile9.3
Maximum14
Range13.9
Interquartile range (IQR)3.71

Descriptive statistics

Standard deviation2.1897036
Coefficient of variation (CV)0.33846003
Kurtosis-1.0528092
Mean6.4696078
Median Absolute Deviation (MAD)1.6
Skewness-0.29017902
Sum5805153.2
Variance4.7948019
MonotonicityNot monotonic
2025-06-06T02:27:22.099749image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8 46318
 
5.1%
3.2 31677
 
3.5%
8.3 24085
 
2.6%
7.9 17477
 
1.9%
7.7 17073
 
1.9%
8.4 15522
 
1.7%
8.1 15030
 
1.6%
8 14613
 
1.6%
3.8 14105
 
1.5%
9.2 14024
 
1.5%
Other values (227) 687372
75.2%
(Missing) 16303
 
1.8%
ValueCountFrequency (%)
0.1 637
 
0.1%
1.93 1771
 
0.2%
2.3 1607
 
0.2%
2.4 1828
 
0.2%
2.5 1373
 
0.2%
2.6 7279
 
0.8%
2.63 41
 
< 0.1%
2.7 1782
 
0.2%
2.8 46318
5.1%
2.9 471
 
0.1%
ValueCountFrequency (%)
14 15
 
< 0.1%
13.23 544
0.1%
11.7 645
0.1%
11.54 1071
0.1%
11.45 551
0.1%
11.4 1223
0.1%
11.24 691
0.1%
11.1 627
0.1%
11 368
 
< 0.1%
10.98 211
 
< 0.1%

Latitude
Real number (ℝ)

High correlation 

Distinct273
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.287473
Minimum53.33
Maximum56.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.178537image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum53.33
5-th percentile53.56
Q153.85
median54.47
Q354.65
95-th percentile54.89
Maximum56.34
Range3.01
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.45826823
Coefficient of variation (CV)0.0084415096
Kurtosis-1.0504922
Mean54.287473
Median Absolute Deviation (MAD)0.34
Skewness-0.13313054
Sum49596981
Variance0.21000977
MonotonicityNot monotonic
2025-06-06T02:27:22.256571image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.87 28797
 
3.2%
54.53 23462
 
2.6%
54.41 20333
 
2.2%
53.84 20044
 
2.2%
53.98 19631
 
2.1%
54.56 17212
 
1.9%
53.54 17009
 
1.9%
53.86 16700
 
1.8%
54.74 16683
 
1.8%
54.57 16667
 
1.8%
Other values (263) 717061
78.5%
ValueCountFrequency (%)
53.33 13
 
< 0.1%
53.34 40
< 0.1%
53.35 29
< 0.1%
53.36 26
< 0.1%
53.37 25
< 0.1%
53.38 27
< 0.1%
53.39 26
< 0.1%
53.4 23
< 0.1%
53.41 24
< 0.1%
53.42 26
< 0.1%
ValueCountFrequency (%)
56.34 2
 
< 0.1%
56.33 4
 
< 0.1%
56.32 4
 
< 0.1%
56.31 3
 
< 0.1%
56.3 3
 
< 0.1%
56.29 12
< 0.1%
56.28 8
< 0.1%
56.27 5
< 0.1%
56.26 5
< 0.1%
56.25 6
< 0.1%

Longitude
Real number (ℝ)

High correlation 

Distinct1285
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.995726
Minimum2.32
Maximum20.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.332976image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum2.32
5-th percentile8.08
Q19.03
median11.08
Q314.12
95-th percentile18.8
Maximum20.66
Range18.34
Interquartile range (IQR)5.09

Descriptive statistics

Standard deviation3.5082469
Coefficient of variation (CV)0.29245807
Kurtosis-0.653744
Mean11.995726
Median Absolute Deviation (MAD)2.4
Skewness0.74748595
Sum10959283
Variance12.307797
MonotonicityNot monotonic
2025-06-06T02:27:22.412229image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.51 4757
 
0.5%
8.52 4514
 
0.5%
8.12 4441
 
0.5%
9.93 4405
 
0.5%
8.11 4178
 
0.5%
9.9 4113
 
0.5%
9.5 4022
 
0.4%
8.51 3619
 
0.4%
8.15 3189
 
0.3%
9.39 3178
 
0.3%
Other values (1275) 873183
95.6%
ValueCountFrequency (%)
2.32 1
 
< 0.1%
7.71 2
 
< 0.1%
7.72 45
< 0.1%
7.73 102
< 0.1%
7.74 99
< 0.1%
7.75 108
< 0.1%
7.76 83
< 0.1%
7.77 83
< 0.1%
7.78 81
< 0.1%
7.79 78
< 0.1%
ValueCountFrequency (%)
20.66 1
 
< 0.1%
20.65 3
< 0.1%
20.64 2
< 0.1%
20.63 2
< 0.1%
20.62 3
< 0.1%
20.61 3
< 0.1%
20.6 2
< 0.1%
20.59 3
< 0.1%
20.58 3
< 0.1%
20.57 3
< 0.1%

SOG
Real number (ℝ)

Distinct227
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.044566
Minimum0.2
Maximum73.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.492324image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile6.5
Q19.8
median11.9
Q314.6
95-th percentile17.5
Maximum73.6
Range73.4
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation3.5403783
Coefficient of variation (CV)0.29393989
Kurtosis0.7935745
Mean12.044566
Median Absolute Deviation (MAD)2.4
Skewness-0.41159347
Sum11003903
Variance12.534279
MonotonicityNot monotonic
2025-06-06T02:27:22.573504image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.4 13193
 
1.4%
10.5 13133
 
1.4%
10.7 12783
 
1.4%
10.6 12764
 
1.4%
10.3 12519
 
1.4%
11 11598
 
1.3%
11.6 11505
 
1.3%
10.9 11485
 
1.3%
10.2 11480
 
1.3%
11.7 11383
 
1.2%
Other values (217) 791756
86.7%
ValueCountFrequency (%)
0.2 442
 
< 0.1%
0.3 1617
0.2%
0.4 1337
0.1%
0.5 1054
0.1%
0.6 958
0.1%
0.7 814
0.1%
0.8 907
0.1%
0.9 843
0.1%
1 771
0.1%
1.1 738
0.1%
ValueCountFrequency (%)
73.6 2
 
< 0.1%
51.2 1
 
< 0.1%
22.6 1
 
< 0.1%
22.5 1
 
< 0.1%
22.4 2
 
< 0.1%
22.3 1
 
< 0.1%
22.2 4
< 0.1%
22.1 4
< 0.1%
22 6
< 0.1%
21.9 5
< 0.1%

COG
Real number (ℝ)

High correlation 

Distinct3601
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.97896
Minimum0
Maximum360
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.653786image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile44
Q181.8
median99.1
Q3134
95-th percentile312
Maximum360
Range360
Interquartile range (IQR)52.2

Descriptive statistics

Standard deviation76.465662
Coefficient of variation (CV)0.61182827
Kurtosis1.3315296
Mean124.97896
Median Absolute Deviation (MAD)21.5
Skewness1.4931655
Sum1.1418066 × 108
Variance5846.9975
MonotonicityNot monotonic
2025-06-06T02:27:22.736966image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
81 7475
 
0.8%
90 7334
 
0.8%
82 6782
 
0.7%
86 5624
 
0.6%
89 5526
 
0.6%
85 5337
 
0.6%
83 5150
 
0.6%
87 4993
 
0.5%
88 4950
 
0.5%
84 4679
 
0.5%
Other values (3591) 855749
93.7%
ValueCountFrequency (%)
0 192
< 0.1%
0.1 290
< 0.1%
0.2 26
 
< 0.1%
0.3 26
 
< 0.1%
0.4 19
 
< 0.1%
0.5 37
 
< 0.1%
0.6 31
 
< 0.1%
0.7 29
 
< 0.1%
0.8 23
 
< 0.1%
0.9 44
 
< 0.1%
ValueCountFrequency (%)
360 12
 
< 0.1%
359.9 43
< 0.1%
359.8 29
< 0.1%
359.7 16
 
< 0.1%
359.6 28
< 0.1%
359.5 28
< 0.1%
359.4 20
< 0.1%
359.3 31
< 0.1%
359.2 22
< 0.1%
359.1 40
< 0.1%

TH
Real number (ℝ)

High correlation 

Distinct361
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.71848
Minimum0
Maximum511
Zeros565
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.0 MiB
2025-06-06T02:27:22.818407image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile45
Q182
median99
Q3136
95-th percentile320
Maximum511
Range511
Interquartile range (IQR)54

Descriptive statistics

Standard deviation89.243589
Coefficient of variation (CV)0.68271595
Kurtosis3.8355682
Mean130.71848
Median Absolute Deviation (MAD)22
Skewness1.9151744
Sum1.1942427 × 108
Variance7964.4182
MonotonicityNot monotonic
2025-06-06T02:27:22.899380image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
82 22874
 
2.5%
81 21301
 
2.3%
83 20883
 
2.3%
90 20022
 
2.2%
84 18941
 
2.1%
85 18885
 
2.1%
89 16462
 
1.8%
86 16055
 
1.8%
88 15375
 
1.7%
80 15244
 
1.7%
Other values (351) 727557
79.6%
ValueCountFrequency (%)
0 565
0.1%
1 410
< 0.1%
2 349
< 0.1%
3 363
< 0.1%
4 262
< 0.1%
5 277
< 0.1%
6 271
< 0.1%
7 314
< 0.1%
8 282
< 0.1%
9 205
 
< 0.1%
ValueCountFrequency (%)
511 12963
1.4%
359 399
 
< 0.1%
358 412
 
< 0.1%
357 337
 
< 0.1%
356 395
 
< 0.1%
355 465
 
0.1%
354 408
 
< 0.1%
353 295
 
< 0.1%
352 265
 
< 0.1%
351 223
 
< 0.1%

Destination
Categorical

High correlation  Imbalance 

Distinct16
Distinct (%)< 0.1%
Missing68
Missing (%)< 0.1%
Memory size54.0 MiB
DEHAM
375027 
PLGDY
291378 
PLGDN
211839 
LTKLJ
 
23353
DESTA
 
3787
Other values (11)
 
8147

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters4567655
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowDEHAM
2nd rowDEHAM
3rd rowDEHAM
4th rowDEHAM
5th rowDEHAM

Common Values

ValueCountFrequency (%)
DEHAM 375027
41.0%
PLGDY 291378
31.9%
PLGDN 211839
23.2%
LTKLJ 23353
 
2.6%
DESTA 3787
 
0.4%
DEKEL 3267
 
0.4%
DEBRV 2439
 
0.3%
RUKGD 1337
 
0.1%
SENOK 540
 
0.1%
SEHAD 334
 
< 0.1%
Other values (6) 230
 
< 0.1%

Length

2025-06-06T02:27:22.967245image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
deham 375027
41.1%
plgdy 291378
31.9%
plgdn 211839
23.2%
ltklj 23353
 
2.6%
desta 3787
 
0.4%
dekel 3267
 
0.4%
debrv 2439
 
0.3%
rukgd 1337
 
0.1%
senok 540
 
0.1%
sehad 334
 
< 0.1%
Other values (6) 230
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
D 889530
19.5%
L 553296
12.1%
G 504554
11.0%
P 503323
11.0%
E 388827
8.5%
A 379148
8.3%
H 375363
8.2%
M 375027
8.2%
Y 291378
 
6.4%
N 212379
 
4.6%
Other values (12) 94830
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4567655
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
D 889530
19.5%
L 553296
12.1%
G 504554
11.0%
P 503323
11.0%
E 388827
8.5%
A 379148
8.3%
H 375363
8.2%
M 375027
8.2%
Y 291378
 
6.4%
N 212379
 
4.6%
Other values (12) 94830
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4567655
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
D 889530
19.5%
L 553296
12.1%
G 504554
11.0%
P 503323
11.0%
E 388827
8.5%
A 379148
8.3%
H 375363
8.2%
M 375027
8.2%
Y 291378
 
6.4%
N 212379
 
4.6%
Other values (12) 94830
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4567655
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
D 889530
19.5%
L 553296
12.1%
G 504554
11.0%
P 503323
11.0%
E 388827
8.5%
A 379148
8.3%
H 375363
8.2%
M 375027
8.2%
Y 291378
 
6.4%
N 212379
 
4.6%
Other values (12) 94830
 
2.1%

Interactions

2025-06-06T02:27:14.479536image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.162385image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.738300image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.377829image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.021995image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.719280image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.324603image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.136953image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.841001image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.654861image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.432831image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.301498image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.971930image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.758478image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.591640image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.270431image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.852387image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.495144image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.154351image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.829439image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.551369image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.254181image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.956626image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.770024image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.548973image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.421139image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.089052image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.886077image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.705801image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.382612image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.969182image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.613048image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.287365image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.942525image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.674262image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.376687image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.169392image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.891120image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.699604image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.540826image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.204511image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.005165image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.816628image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.488316image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.080808image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.716049image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.405051image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.048652image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.790514image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.495766image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.312976image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.004820image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.811416image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
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2025-06-06T02:27:04.454001image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.126992image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.942233image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.776722image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.431758image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.253017image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:15.081238image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.711341image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.310657image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.943405image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.653892image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.274301image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.025274image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.730206image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.576940image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.251881image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
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2025-06-06T02:26:52.832676image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.434044image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.068532image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.791688image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.405292image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.149753image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.851026image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.704292image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.401567image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.330119image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.021948image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.689748image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.506768image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:15.344647image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:52.949630image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.555111image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.194630image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:57.916975image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.522640image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.275985image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.969165image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.826518image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.524707image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.456063image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.147668image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.811064image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.636617image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:15.460559image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.063693image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.673073image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.315441image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.029460image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.636717image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.393873image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.090042image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:04.942921image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.651470image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.580848image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.265056image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:11.930397image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.758955image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:15.582467image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.178528image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.787173image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.429344image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.145137image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.747809image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.520333image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.227133image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.060162image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.818208image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.704875image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.382862image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.140000image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.880322image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
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2025-06-06T02:26:53.290355image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:54.902568image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.539270image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.257550image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.859441image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.637919image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.350306image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.181502image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:06.948101image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.819947image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.497149image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.269351image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:13.997081image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:15.897536image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.406615image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.021049image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.656496image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.373772image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:59.974771image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.761018image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.473624image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.300392image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.072481image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:08.942809image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.620005image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.390410image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.123630image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
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2025-06-06T02:26:56.769170image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.486262image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.091959image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:01.886268image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.597363image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.419705image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.196758image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.058165image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.738773image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.518361image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.239172image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:16.164975image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:53.630410image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:55.264402image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:56.895122image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:26:58.606866image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:00.205506image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:02.010766image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:03.721315image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:05.538213image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:07.318793image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:09.180972image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:10.858067image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:12.645063image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
2025-06-06T02:27:14.367331image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/

Correlations

2025-06-06T02:27:23.106788image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
BreadthCOGDestinationDraughtEndLatitudeEndLongitudeEndPortLatitudeLengthLongitudeSOGStartLatitudeStartLongitudeStartPortTHTripIDshiptype
Breadth1.0000.0340.1980.9020.079-0.2720.407-0.0870.937-0.0970.433-0.037-0.1830.4070.020-0.0210.300
COG0.0341.0000.1670.001-0.290-0.2900.460-0.344-0.005-0.1230.001-0.323-0.3070.4600.9370.0070.034
Destination0.1980.1671.0000.1830.6980.9950.9950.3540.2320.3270.1260.4480.7040.9950.1550.1600.170
Draught0.9020.0010.1831.0000.129-0.1710.261-0.0100.914-0.0190.4250.078-0.0950.261-0.0060.0210.333
EndLatitude0.079-0.2900.6980.1291.0000.5251.0000.7510.1520.736-0.0200.8150.7981.000-0.283-0.025-0.100
EndLongitude-0.272-0.2900.995-0.1710.5251.0001.0000.719-0.1580.731-0.2680.8130.8171.000-0.2620.009-0.038
EndPort0.4070.4600.9950.2611.0001.0001.0001.0000.3990.9190.1011.0001.0001.0000.3970.2630.229
Latitude-0.087-0.3440.354-0.0100.7510.7191.0001.0000.0050.800-0.0430.8130.7971.000-0.322-0.013-0.074
Length0.937-0.0050.2320.9140.152-0.1580.3990.0051.000-0.0010.4280.099-0.1010.399-0.015-0.0510.273
Longitude-0.097-0.1230.327-0.0190.7360.7310.9190.800-0.0011.000-0.2110.8140.7980.919-0.111-0.013-0.080
SOG0.4330.0010.1260.425-0.020-0.2680.101-0.0430.428-0.2111.000-0.113-0.1910.101-0.025-0.0580.190
StartLatitude-0.037-0.3230.4480.0780.8150.8131.0000.8130.0990.814-0.1131.0000.8421.000-0.304-0.012-0.071
StartLongitude-0.183-0.3070.704-0.0950.7980.8171.0000.797-0.1010.798-0.1910.8421.0001.000-0.286-0.004-0.089
StartPort0.4070.4600.9950.2611.0001.0001.0001.0000.3990.9190.1011.0001.0001.0000.3970.2630.229
TH0.0200.9370.155-0.006-0.283-0.2620.397-0.322-0.015-0.111-0.025-0.304-0.2860.3971.0000.0070.033
TripID-0.0210.0070.1600.021-0.0250.0090.263-0.013-0.051-0.013-0.058-0.012-0.0040.2630.0071.0000.148
shiptype0.3000.0340.1700.333-0.100-0.0380.229-0.0740.273-0.0800.190-0.071-0.0890.2290.0330.1481.000

Missing values

2025-06-06T02:27:16.304284image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
A simple visualization of nullity by column.
2025-06-06T02:27:17.616306image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-06-06T02:27:19.626019image/svg+xmlMatplotlib v3.10.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TripIDStartLatitudeStartLongitudeStartTimeEndLatitudeEndLongitudeEndTimeStartPortEndPorttimeshiptypeLengthBreadthDraughtLatitudeLongitudeSOGCOGTHDestination
03913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:07:00+00:0071.0277.042.011.5453.578.530.7331.2143DEHAM
13913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:10:00+00:0071.0277.042.011.5453.578.531.6315.3117DEHAM
23913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:10:00+00:0071.0277.042.011.5453.578.532.8322.6100DEHAM
33913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:12:00+00:0071.0277.042.011.5453.578.532.8286.374DEHAM
43913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:16:00+00:0071.0277.042.011.5453.578.534.3333.1333DEHAM
53913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:17:00+00:0071.0277.042.011.5453.578.535.2334.0333DEHAM
63913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:18:00+00:0071.0277.042.011.5453.578.535.7333.0333DEHAM
73913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:19:00+00:0071.0277.042.011.5453.578.526.3333.0333DEHAM
83913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:20:00+00:0071.0277.042.011.5453.588.526.8333.0333DEHAM
93913153.578.532016-01-24 08:06:00+00:0053.539.92016-01-24 16:44:00+00:00BREMERHAVENHAMBURG2016-01-24 08:21:00+00:0071.0277.042.011.5453.588.527.1332.1333DEHAM
TripIDStartLatitudeStartLongitudeStartTimeEndLatitudeEndLongitudeEndTimeStartPortEndPorttimeshiptypeLengthBreadthDraughtLatitudeLongitudeSOGCOGTHDestination
913589220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:02:00+00:0070.089.013.04.054.5018.747.2222.0215PLGDN
913590220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:01:00+00:0070.089.013.04.054.5018.747.2221.6215PLGDN
913591220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 14:00:00+00:0070.089.013.04.054.5018.747.2221.5215PLGDN
913592220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:59:00+00:0070.089.013.04.054.5018.757.2221.2215PLGDN
913593220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:58:00+00:0070.089.013.04.054.5018.757.2220.8215PLGDN
913594220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:57:00+00:0070.089.013.04.054.5118.757.2221.0215PLGDN
913595220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:56:00+00:0070.089.013.04.054.5118.757.2221.9215PLGDN
913596220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:55:00+00:0070.089.013.04.054.5118.757.2222.1215PLGDN
913597220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:54:00+00:0070.089.013.04.054.5118.767.2221.2215PLGDN
913598220404954.3610.142017-04-03 07:54:00+00:0054.3818.662017-04-04 15:28:00+00:00KIELGDYNIA2017-04-04 13:53:00+00:0070.089.013.04.054.5118.767.2221.0214PLGDN